metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- f1
widget:
- text: What could possibly go wrong?
- text: We may have faith that human inventiveness will prevail in the long run.
- text: That can happen again.
- text: But in fact it was intensely rational.
- text: Chinese crime, like Chinese cuisine, varies according to regional origin.
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: f1
value: 0.7526132404181185
name: F1
SetFit
This is a SetFit model that can be used for Text Classification. A SVC instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Classification head: a SVC instance
- Maximum Sequence Length: 256 tokens
- Number of Classes: 2 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
Label | Examples |
---|---|
SUBJ |
|
OBJ |
|
Evaluation
Metrics
Label | F1 |
---|---|
all | 0.7526 |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("SOUMYADEEPSAR/Setfit_subj_SVC")
# Run inference
preds = model("That can happen again.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 3 | 35.9834 | 97 |
Label | Training Sample Count |
---|---|
OBJ | 117 |
SUBJ | 124 |
Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (5, 5)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (1e-05, 1e-05)
- head_learning_rate: 1e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0008 | 1 | 0.3862 | - |
0.0415 | 50 | 0.4092 | - |
0.0830 | 100 | 0.3596 | - |
0.1245 | 150 | 0.2618 | - |
0.1660 | 200 | 0.2447 | - |
0.2075 | 250 | 0.263 | - |
0.2490 | 300 | 0.2583 | - |
0.2905 | 350 | 0.3336 | - |
0.3320 | 400 | 0.2381 | - |
0.3734 | 450 | 0.2454 | - |
0.4149 | 500 | 0.259 | - |
0.4564 | 550 | 0.2083 | - |
0.4979 | 600 | 0.2437 | - |
0.5394 | 650 | 0.2231 | - |
0.5809 | 700 | 0.0891 | - |
0.6224 | 750 | 0.1164 | - |
0.6639 | 800 | 0.0156 | - |
0.7054 | 850 | 0.0394 | - |
0.7469 | 900 | 0.0065 | - |
0.7884 | 950 | 0.0024 | - |
0.8299 | 1000 | 0.0012 | - |
0.8714 | 1050 | 0.0014 | - |
0.9129 | 1100 | 0.0039 | - |
0.9544 | 1150 | 0.0039 | - |
0.9959 | 1200 | 0.001 | - |
1.0373 | 1250 | 0.0007 | - |
1.0788 | 1300 | 0.0003 | - |
1.1203 | 1350 | 0.001 | - |
1.1618 | 1400 | 0.0003 | - |
1.2033 | 1450 | 0.0003 | - |
1.2448 | 1500 | 0.0014 | - |
1.2863 | 1550 | 0.0003 | - |
1.3278 | 1600 | 0.0003 | - |
1.3693 | 1650 | 0.0001 | - |
1.4108 | 1700 | 0.0004 | - |
1.4523 | 1750 | 0.0003 | - |
1.4938 | 1800 | 0.0008 | - |
1.5353 | 1850 | 0.0002 | - |
1.5768 | 1900 | 0.0005 | - |
1.6183 | 1950 | 0.0002 | - |
1.6598 | 2000 | 0.0004 | - |
1.7012 | 2050 | 0.0001 | - |
1.7427 | 2100 | 0.0002 | - |
1.7842 | 2150 | 0.0002 | - |
1.8257 | 2200 | 0.0002 | - |
1.8672 | 2250 | 0.0003 | - |
1.9087 | 2300 | 0.0001 | - |
1.9502 | 2350 | 0.0002 | - |
1.9917 | 2400 | 0.0001 | - |
2.0332 | 2450 | 0.0003 | - |
2.0747 | 2500 | 0.0002 | - |
2.1162 | 2550 | 0.0001 | - |
2.1577 | 2600 | 0.0001 | - |
2.1992 | 2650 | 0.0004 | - |
2.2407 | 2700 | 0.0002 | - |
2.2822 | 2750 | 0.0001 | - |
2.3237 | 2800 | 0.0005 | - |
2.3651 | 2850 | 0.0002 | - |
2.4066 | 2900 | 0.0003 | - |
2.4481 | 2950 | 0.0001 | - |
2.4896 | 3000 | 0.0001 | - |
2.5311 | 3050 | 0.0001 | - |
2.5726 | 3100 | 0.0001 | - |
2.6141 | 3150 | 0.0002 | - |
2.6556 | 3200 | 0.0001 | - |
2.6971 | 3250 | 0.0002 | - |
2.7386 | 3300 | 0.0002 | - |
2.7801 | 3350 | 0.0001 | - |
2.8216 | 3400 | 0.0001 | - |
2.8631 | 3450 | 0.0001 | - |
2.9046 | 3500 | 0.0001 | - |
2.9461 | 3550 | 0.0 | - |
2.9876 | 3600 | 0.0002 | - |
3.0290 | 3650 | 0.0001 | - |
3.0705 | 3700 | 0.0 | - |
3.1120 | 3750 | 0.0001 | - |
3.1535 | 3800 | 0.0001 | - |
3.1950 | 3850 | 0.0001 | - |
3.2365 | 3900 | 0.0001 | - |
3.2780 | 3950 | 0.0001 | - |
3.3195 | 4000 | 0.0001 | - |
3.3610 | 4050 | 0.0001 | - |
3.4025 | 4100 | 0.0 | - |
3.4440 | 4150 | 0.0001 | - |
3.4855 | 4200 | 0.0001 | - |
3.5270 | 4250 | 0.0001 | - |
3.5685 | 4300 | 0.0001 | - |
3.6100 | 4350 | 0.0002 | - |
3.6515 | 4400 | 0.0001 | - |
3.6929 | 4450 | 0.0001 | - |
3.7344 | 4500 | 0.0 | - |
3.7759 | 4550 | 0.0 | - |
3.8174 | 4600 | 0.0001 | - |
3.8589 | 4650 | 0.0001 | - |
3.9004 | 4700 | 0.0001 | - |
3.9419 | 4750 | 0.0 | - |
3.9834 | 4800 | 0.0001 | - |
4.0249 | 4850 | 0.0001 | - |
4.0664 | 4900 | 0.0001 | - |
4.1079 | 4950 | 0.0001 | - |
4.1494 | 5000 | 0.0 | - |
4.1909 | 5050 | 0.0 | - |
4.2324 | 5100 | 0.0 | - |
4.2739 | 5150 | 0.0 | - |
4.3154 | 5200 | 0.0001 | - |
4.3568 | 5250 | 0.0001 | - |
4.3983 | 5300 | 0.0001 | - |
4.4398 | 5350 | 0.0 | - |
4.4813 | 5400 | 0.0001 | - |
4.5228 | 5450 | 0.0 | - |
4.5643 | 5500 | 0.0001 | - |
4.6058 | 5550 | 0.0001 | - |
4.6473 | 5600 | 0.0001 | - |
4.6888 | 5650 | 0.0 | - |
4.7303 | 5700 | 0.0001 | - |
4.7718 | 5750 | 0.0001 | - |
4.8133 | 5800 | 0.0001 | - |
4.8548 | 5850 | 0.0 | - |
4.8963 | 5900 | 0.0 | - |
4.9378 | 5950 | 0.0 | - |
4.9793 | 6000 | 0.0001 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.1
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
Citation
BibTeX
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}